More fun with lagrange multipliers

In summary, the problem is that the constraint equations are incorrect. The correct equations are 2\mu + \lambda = 12.
  • #1
hiigaranace
10
0

Homework Statement



Find the point closest to the origin on the line of intersection of the planes y + 2z = 12 and x + y = 6

Homework Equations



[tex]\nu[/tex]f = [tex]\lambda\nu[/tex]g1 +[tex]\mu\nu[/tex]g2
f = x2+y2+z2
g1: y + 2z = 12
g2: x + y = 6

There are supposed to be gradients on all of those, whether or not LaTeX wants to show them.

The Attempt at a Solution



Let [tex]\nu[/tex]f(x, y, z) = 2x[tex]\vec{i}[/tex]+2y[tex]\vec{j}[/tex]+2z[tex]\vec{k}[/tex], [tex]\nu[/tex]g1(x, y, z) = [tex]\vec{j}[/tex]+2[tex]\vec{k}[/tex], and [tex]\nu[/tex]g2(x, y, z) = [tex]\vec{i}[/tex]+[tex]\vec{j}[/tex]

this gives:

2x = [tex]\mu[/tex] 2y = [tex]\lambda + \mu[/tex] 2z = [tex]2\lambda[/tex]

I tried pushing ahead from here, but I end up getting nowhere. Can someone please help me?
...annnnnnnnnnnd much as I hate to admit it, I'm having a lot of trouble with lagrange multipliers in the first place, and my textbook is sadly not a whole lot of help. If anyone out there can explain how to work these out in a general sense, I would very much appreciate it.
 
Last edited:
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  • #2
It looks good so far (you probably should put some commas into make your solution easier to read). You should apply the constraints to the solution to determine the Lagrange multipliers.
 
  • #3
fzero said:
It looks good so far (you probably should put some commas into make your solution easier to read). You should apply the constraints to the solution to determine the Lagrange multipliers.

Soooo...plug the lambda and mu back into the equation I'm trying to optimize?

EDIT: When I try to trn the constraints into lambda and mu, I wind up with:

2[tex]\mu[/tex] + [tex]\lambda[/tex] -12 = 0 3[tex]\lambda[/tex]+[tex]\mu[/tex]-12=0

combine them, and you get

[tex]\mu[/tex] = 2[tex]\lambda[/tex]

so, x = 1/2[tex]\mu[/tex] = [tex]\lambda[/tex] = z y = 3/2 [tex]\lambda[/tex]

which ends up giving me 5[tex]\lambda[/tex] = 12.

Problem is that this is wrong. It's supposed to come out as point = (2, 4, 4). I don't understand what I'm doing wrong.
 
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  • #4
You have 3 equations and 5 unknowns. You need 2 additional equations relating the unknowns - the constraint equations.
 
  • #5
hotvette said:
You have 3 equations and 5 unknowns. You need 2 additional equations relating the unknowns - the constraint equations.

I'm sorry, I don't follow. I thought the constraint equations were:

g1=y+2z=12
g2=x+y=6

The other equations I have are:

f = x2+y2+z2
[tex]\grad{f}[/tex] = 2x+2y+2z
[tex]\grad{g1}[/tex] = [tex]\vec{j}[/tex] + 2[tex]\vec{k}[/tex]
[tex]\grad{g2}[/tex] = [tex]\vec{i}[/tex] + [tex]\vec{j}[/tex]

if these are what you had in mind, then I'm afraid I don't see how to go forward without running into the same problems as before.
 
  • #6
hiigaranace said:
Soooo...plug the lambda and mu back into the equation I'm trying to optimize?

EDIT: When I try to trn the constraints into lambda and mu, I wind up with:

2[tex]\mu[/tex] + [tex]\lambda[/tex] -12 = 0 3[tex]\lambda[/tex]+[tex]\mu[/tex]-12=0

There's a mistake in your 2nd equation, it should be [tex]2\mu + \lambda =12[/tex]. I find (2,4,4). Just recheck your algebra.
 
  • #7
Ah, crud, I see it now. Blasted algebra always gets me.
Thanks for the help!
 

1. What are Lagrange multipliers?

Lagrange multipliers are a mathematical tool used in optimization problems to find the maximum or minimum of a function subject to constraints. They are named after Joseph-Louis Lagrange, who first introduced them in the 18th century.

2. How do Lagrange multipliers work?

Lagrange multipliers work by introducing a new variable, called the multiplier, to the objective function and the constraints. This allows for the constraints to be incorporated into the objective function, making it easier to find the maximum or minimum value.

3. What are some applications of Lagrange multipliers?

Lagrange multipliers are commonly used in economics, physics, and engineering to solve optimization problems with constraints. They are also used in machine learning and data science to find the best parameters for a model.

4. What is the general formula for Lagrange multipliers?

The general formula for Lagrange multipliers is:
∇f(x, y, z) = λ∇g(x, y, z), where f(x, y, z) is the objective function, g(x, y, z) is the constraint function, and λ is the Lagrange multiplier.

5. Are there any limitations to using Lagrange multipliers?

One limitation of using Lagrange multipliers is that they can only be used for optimization problems with continuous variables and constraints. They also may not always provide the global maximum or minimum, but rather a local one. Additionally, the method may become more complex for problems with multiple constraints.

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